def set_network(depth, ctx, lr, beta1, ndf, ngf,latent, append=True, solver='adam'):
# Pixel2pixel networks
if append:
netD = models.Discriminator(in_channels=6, n_layers =2 , ndf=ndf)##netG = models.CEGenerator(in_channels=3, n_layers=depth, ndf=ngf) # UnetGenerator(in_channels=3, num_downs=8) #
netD2 = models.LatentDiscriminator(in_channels=6, n_layers =2 , ndf=ndf)##netG = models.CEGenerator(in_channels=3, n_layers=depth, ndf=ngf) # UnetGenerator(in_channels=3, num_downs=8) #
else:
netD = models.Discriminator(in_channels=3, n_layers =2 , ndf=ndf)
netD2 = models.LatentDiscriminator(in_channels=3, n_layers =2 , ndf=ndf)
#netG = models.UnetGenerator(in_channels=3, num_downs =depth, ngf=ngf) # UnetGenerator(in_channels=3, num_downs=8) #
#netD = models.Discriminator(in_channels=6, n_layers =depth-1, ndf=ngf/4)
netEn = models.Encoder(in_channels=3, n_layers =depth,latent=latent, ndf=ngf)
netDe = models.Decoder(in_channels=3, n_layers =depth, latent=latent, ndf=ngf)
# Initialize parameters
models.network_init(netEn, ctx=ctx)
models.network_init(netDe, ctx=ctx)
models.network_init(netD, ctx=ctx)
models.network_init(netD2, ctx=ctx)
trainerEn = gluon.Trainer(netEn.collect_params(), 'adam', {'learning_rate': lr, 'beta1': beta1})
trainerDe = gluon.Trainer(netDe.collect_params(), 'adam', {'learning_rate': lr, 'beta1': beta1})
trainerD = gluon.Trainer(netD.collect_params(), 'adam', {'learning_rate': lr, 'beta1': beta1})
trainerD2 = gluon.Trainer(netD2.collect_params(), 'adam', {'learning_rate': lr, 'beta1': beta1})
return netEn, netDe, netD, netD2, trainerEn, trainerDe, trainerD, trainerD2
def set_network(depth, ctx, lr, beta1, ndf, ngf, append=True):
if append:
netD = models.Discriminator(in_channels=6,
n_layers=depth - 1,
istest=True,
ndf=ndf)
else:
netD = models.Discriminator(in_channels=3,
n_layers=depth - 1,
istest=True,
ndf=ndf)
netEn = models.Encoder(
in_channels=3, n_layers=depth, istest=True, latent=512,
ndf=ngf) # UnetGenerator(in_channels=3, num_downs=8) #
netDe = models.Decoder(
in_channels=3, n_layers=depth, istest=True, latent=512,
ndf=ngf) # UnetGenerator(in_channels=3, num_downs=8) #
netD2 = models.LatentDiscriminator(in_channels=6, n_layers=2, ndf=ndf)
netDS = models.Discriminator(in_channels=3, n_layers=2, ndf=64)
return netEn, netDe, netD, netD2, netDS
